Abstract
One of the most significant pandemics has been raised in the form of Coronavirus disease 2019 (COVID19). Many researchers have faced various types of challenges for finding the accurate model, which can automatically detect the COVID-19 using computed pulmonary tomography (CT) scans of the chest. This paper has also focused on the same area, and a fully automatic model has been developed, which can predict the COVID-19 using the chest CT scans. The performance of the proposed method has been evaluated by classifying the CT scans of community-acquired pneumonia (CAP) and other non-pneumonia. The proposed deep learning model is based on ResNet 50, named CORNet for the detection of COVID-19, and also performed the retrospective and multicenter analysis for the extraction of visual characteristics from volumetric chest CT scans during COVID-19 detection. Between August 2016 and May 2020, the datasets were obtained from six hospitals. Results are evaluated on the image dataset consisting of a total of 10,052 CT scan images generated from 7850 patients, and the average age of the patients was 50 years. The implemented model has achieved the sensitivity and specificity of 90% and 96%, per scanned image with an AUC of 0. 95.